Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

System design round at Anthropic for a software engineer role. The whole session was basically one massive question about distributed statistics at scale, which sounds straightforward until you're 20 minutes in and realizing you forgot to talk about fault tolerance.

Questions Asked (1)

You have hundreds of billions of records spread across many machines. Design a system to compute the global median and global mode. Cover data partitioning, local summaries, how reducers combine partial results, and how you keep network I/O low. Compare exact vs approximate methods, handle skew and faults, support incremental updates, and extend the design to a sliding-window version over an unbounded stream.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This is a lot of surface area for one question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (exactness, latency, update frequency) and then present a two-phase MapReduce-style design: local summaries (histograms/sketches) per partition, followed by a merge phase that combines them to produce global median and mode. Discuss exact vs approximate trade-offs, skew handling, fault tolerance, and extend to sliding windows using incremental aggregation and decay.

Pro tip: Emphasize that median and mode are not decomposable like sum; you must either shuffle all data (exact) or use mergeable summaries (approximate). Show you understand the cost of exactness and when approximation is acceptable.

1. Clarify requirements and constraints

Ask about data distribution, value types (numeric/categorical), required accuracy, latency, update frequency, and whether the stream is bounded or unbounded.

2. Design data partitioning and local summaries

Partition data by key or range; each machine computes a local summary: for median, a histogram or quantile sketch; for mode, a frequency map or count-min sketch.

3. Combine partial results in reducers

Merge local summaries: for median, merge histograms and compute the global median via binary search on value ranges; for mode, merge frequency maps and sum counts, handling collisions.

4. Optimize network I/O and handle skew/faults

Use compact sketches (e.g., t-digest, KLL) to reduce data transfer; handle skew by sampling or adaptive partitioning; ensure fault tolerance via replication and checkpointing.

5. Extend to incremental and sliding-window streams

For incremental updates, maintain mergeable summaries and update them as new data arrives; for sliding windows, use exponential decay or maintain multiple time-based buckets and expire old data.

Key Points to Mention

Exact vs approximate methods: exact requires shuffling all data (O(N) network), approximate uses sketches (e.g., t-digest for quantiles, count-min sketch for frequencies) with bounded error.
Mergeability of summaries: histograms and frequency maps are mergeable; sketches like t-digest and KLL are designed for distributed merging.
Skew handling: use sampling to detect heavy hitters, or adaptive partitioning to avoid hotspots; for mode, consider space-saving algorithm for heavy hitters.
Fault tolerance: replicate partitions, checkpoint summaries, and use idempotent merges to recover from failures.
Incremental updates: maintain summaries in a distributed store (e.g., Redis) and update them with each new record; use commutative/associative operations for easy merging.
Sliding window: use time-based buckets (e.g., per minute) and aggregate over the last N buckets; or use exponential decay to weight recent data more heavily.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.